A curve interpretation and diagnostic technique for industrial processes

Detecting manufacturing problems as soon as they occur is important for efficient manufacturing in today's factories. Many of these problems could be minimized by installing diagnostic systems to monitor manufacturing steps. A diagnostic technique has been developed to analyze process parameters and observables that change over time. Process parameters control the operation of equipment, and observables are attributes of a partially completed product. The technique uses a specified digital signal processing algorithm known as dynamic time warping (DTW) to transform the input signal into symbolic data. Knowledge-based diagnosis is performed on the symbolic data to determine malfunctions. A detailed description of the DTW algorithm and knowledge-based analysis is presented. Two different applications-one in the glass industry and another one in the semiconductor industry-are discussed to illustrate the general use of this technique.<
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